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Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives

Corporation de l'École Polytechnique de Montréal

$158,400.00

Use of AI to speed up numerical simulations: application to the design of superconducting tapes for energy and medicine domains

FY 2019-20 · 2020-03-27

Summary
Ceramic high temperature superconductors (HTS) are now routinely fabricated in the form of long lengths of tapes (several km), making it possible to envision large-scale superconducting applications. One issue concerns the physical properties of those HTS tapes, which can vary over length scales shorter than 1 millimeter, and lead to destructive hot spots in some circumstances. A strategy to find a solution to this problem at the design stage is to simulate the complete transient electrothermal behavior of long lengths of HTS tapes in real operating conditions while retaining their microscopic features. However, time transient simulations requires tremendous calculation times. AI techniques has proven useful for speeding up numeric simulations, but there are still open problems from the point of how to best use AI approaches due to the diversity and complexity of the physical systems object of the simulation. Because of its specificities, this particular application poses several challenges to the introduction of AI techniques, which makes it suitable from the point of view of both core AI research and material science. The AI solutions would benefit similar problems in other domains and the improvements in the design of superconductive tapes will find applications in many important areas, where high field magnets are required.
Program
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Location
Montreal|Montréal, Quebec, Canada
Recipient Type
Academia
Business Number
107585226
Type
Grant
Source
Proactive Disclosure - Grants and Contributions

Amendments

Each version of the agreement as published, oldest first, with its total as of that version. The amount above is the latest.

AmendmentDateFiscal YearTotal
02020-03-272019-20$158,400.00
22020-03-272019-20$158,400.00

As published

Additional Information
This agreement has been amended 2 time(s). The end date of this agreement has been modified by 364 days.
Additional Information (French)
Cet accord a été modifié 2 fois. La date de fin du présent accord a été changé de 364 jours.
Agreement End Date
2023-03-31
Agreement Number
947573
Agreement Start Date
2020-03-27
Agreement Title
Use of AI to speed up numerical simulations: application to the design of superconducting tapes for energy and medicine domains
Agreement Title (French)
Exploitation de l'IA pour accélérer les simulations numériques : Application à la conception de rubans supraconducteurs dans les secteurs de l'énergie et de la médecine
Agreement Type
Grant
Agreement Value
$158,400.00
Amendment Date
2022-08-16
Amendment Number
2
Description
Ceramic high temperature superconductors (HTS) are now routinely fabricated in the form of long lengths of tapes (several km), making it possible to envision large-scale superconducting applications. One issue concerns the physical properties of those HTS tapes, which can vary over length scales shorter than 1 millimeter, and lead to destructive hot spots in some circumstances. A strategy to find a solution to this problem at the design stage is to simulate the complete transient electrothermal behavior of long lengths of HTS tapes in real operating conditions while retaining their microscopic features. However, time transient simulations requires tremendous calculation times. AI techniques has proven useful for speeding up numeric simulations, but there are still open problems from the point of how to best use AI approaches due to the diversity and complexity of the physical systems object of the simulation. Because of its specificities, this particular application poses several challenges to the introduction of AI techniques, which makes it suitable from the point of view of both core AI research and material science. The AI solutions would benefit similar problems in other domains and the improvements in the design of superconductive tapes will find applications in many important areas, where high field magnets are required.
Description (French)
Les supraconducteurs céramiques à haute température (HTS) sont désormais systématiquement fabriqués sous la forme de longues bandes (plusieurs km), permettant d'envisager des applications supraconductrices à grande échelle. Un problème concerne les propriétés physiques de ces bandes HTS, qui peuvent varier sur des échelles de longueur inférieures à 1 millimètre, et conduire à des points chauds destructeurs dans certaines circonstances. Une stratégie pour trouver une solution à ce problème au stade de la conception consiste à simuler le comportement électrothermique transitoire complet de longues bandes de bandes HTS dans des conditions de fonctionnement réelles tout en conservant leurs caractéristiques microscopiques. Cependant, les simulations transitoires temporelles nécessitent des temps de calcul énormes. Les techniques d'IA se sont révélées utiles pour accélérer les simulations numériques, mais il reste des problèmes ouverts quant à la meilleure façon d'utiliser les approches d'IA en raison de la diversité et de la complexité des systèmes physiques objet de la simulation. En raison de ses spécificités, cette application particulière pose plusieurs défis à l'introduction des techniques d'IA, ce qui la rend appropriée du point de vue de la recherche fondamentale en IA et de la science des matériaux. Les solutions d'IA bénéficieraient de problèmes similaires dans d'autres domaines et les améliorations de la conception des bandes supraconductrices trouveront des applications dans de nombreux domaines importants, où des aimants à champ élevé sont nécessaires.
Expected Results
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Expected Results (French)
À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides.
Federal Riding
Outremont
Federal Riding (French)
Outremont
Federal Riding Number
24054
NAICS Code
541710
Organization Code
nrc-cnrc
Organization
National Research Council Canada | Conseil national de recherches Canada
Program Name
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Name (French)
Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D
Program Purpose
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
Program Purpose (French)
Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires.
Recipient Business Number
107585226
Recipient City
Montreal|Montréal
Recipient Country
CA
Recipient Legal Name
Corporation de l'École Polytechnique de Montréal
Recipient Postal Code
H3T 1J4
Recipient Province or Territory
QC
Recipient Type
S
Reference Number
172-2019-2020-Q4-947573
Corporation de l'École Polytechnique de Montréal - $1… | Canada Spends